{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supervised learning of tabular data using AutoML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "* [Introduction](#Introduction)\n",
    "* [Prepare tabular data](#prepare)\n",
    "* [Train model using AutoML](#AutoML)\n",
    "* [Reload trained model for prediction](#Reload)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "<a id='Introduction'></a> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pipeline of activities in a typical machine learning project involves data preprocessing, exploratory data analysis, feature selection/feature engineering, model selection, hyper parameter tuning, generating model explanation and model selection/evaluation. This is an iterative process and data scientists spend a lot of time going through multiple iterations of this pipeline before they are able to identify the best model. AutoML aims to automates this workflow. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`arcgis.learn` users will now be able to use AutoML for supervised learning classification or regression problems involving tabular data. The AutoML implementation in `arcgis.learn` builds upon the implementation from MLJar (https://github.com/mljar/mljar-supervised) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare tabular data\n",
    "<a id='prepare'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data can be [feature layer](https://developers.arcgis.com/python/guide/working-with-feature-layers-and-features/), [spatially enabled dataframe](https://developers.arcgis.com/python/guide/introduction-to-the-spatially-enabled-dataframe/) with/without rasters or just a simple dataframe. The data for AutoML is prepared the same way it is prepared for [supervised learning ML Models](https://developers.arcgis.com/python/guide/ml-and-dl-on-tabular-data/).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from IPython.display import Image, HTML\n",
    "import arcgis\n",
    "from arcgis.gis import GIS\n",
    "from arcgis.learn import prepare_tabulardata,AutoML\n",
    "from sklearn.preprocessing import MinMaxScaler,RobustScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will be taking a feature layer hosted on ArcGIS Online, convert it to a spatially enabled dataframe and prepare the data using prepare_tabulardata method from `arcgis.learn`. More details about data preparation for ML Models can be found [here](https://developers.arcgis.com/python/guide/ml-and-dl-on-tabular-data/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "gis = GIS('home')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=adaead8cb3174ac6a89f0c14ae70aadd' target='_blank'>\n",
       "                        <img src='' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=adaead8cb3174ac6a89f0c14ae70aadd' target='_blank'><b>calgary_no_southland_solar</b>\n",
       "                        </a>\n",
       "                        <br/><br/><img src='https://geosaurus.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\" width=16 height=16>Feature Layer Collection by api_data_owner\n",
       "                        <br/>Last Modified: October 23, 2020\n",
       "                        <br/>0 comments, 11,832 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"calgary_no_southland_solar\" type:Feature Layer Collection owner:api_data_owner>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calgary_no_southland_solar = gis.content.search('calgary_no_southland_solar owner:api_data_owner', 'feature layer')[0]\n",
    "calgary_no_southland_solar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>ID</th>\n",
       "      <th>solar_plan</th>\n",
       "      <th>altitude_m</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>dayl__s_</th>\n",
       "      <th>prcp__mm_d</th>\n",
       "      <th>srad__W_m_</th>\n",
       "      <th>swe__kg_m_</th>\n",
       "      <th>tmax__deg</th>\n",
       "      <th>tmin__deg</th>\n",
       "      <th>vp__Pa_</th>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017-12-27</td>\n",
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       "      <td>Glenmore Water Treatment Plant</td>\n",
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      "text/plain": [
       "   FID       date      ID                      solar_plan  altitude_m  \\\n",
       "0    1 2017-12-24  355827  Glenmore Water Treatment Plant        1095   \n",
       "1    2 2017-12-25  355827  Glenmore Water Treatment Plant        1095   \n",
       "2    3 2017-12-26  355827  Glenmore Water Treatment Plant        1095   \n",
       "3    4 2017-12-27  355827  Glenmore Water Treatment Plant        1095   \n",
       "4    5 2017-12-28  355827  Glenmore Water Treatment Plant        1095   \n",
       "\n",
       "    latitude   longitude  wind_speed  dayl__s_  prcp__mm_d  srad__W_m_  \\\n",
       "0  51.003078 -114.100571     7.20467   27648.0           1  108.800003   \n",
       "1  51.003078 -114.100571    3.385235   27648.0           1  115.199997   \n",
       "2  51.003078 -114.100571    5.076316   27648.0           0  118.400002   \n",
       "3  51.003078 -114.100571    5.617623   27648.0           0        96.0   \n",
       "4  51.003078 -114.100571    2.561512   27648.0           0  118.400002   \n",
       "\n",
       "   swe__kg_m_  tmax__deg  tmin__deg  vp__Pa_  kWh_filled  capacity_f  \\\n",
       "0          12      -10.5      -21.0      120    1.242357    0.000177   \n",
       "1          12      -18.0      -29.5       40    2.477714    0.000354   \n",
       "2          12      -20.0      -32.0       40    3.713071     0.00053   \n",
       "3          12      -18.0      -26.5       80    4.948429    0.000707   \n",
       "4          12      -17.0      -28.5       40    6.183786    0.000883   \n",
       "\n",
       "                                               SHAPE  \n",
       "0  {\"x\": -12701617.407282012, \"y\": 6621838.159138...  \n",
       "1  {\"x\": -12701617.407282012, \"y\": 6621838.159138...  \n",
       "2  {\"x\": -12701617.407282012, \"y\": 6621838.159138...  \n",
       "3  {\"x\": -12701617.407282012, \"y\": 6621838.159138...  \n",
       "4  {\"x\": -12701617.407282012, \"y\": 6621838.159138...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calgary_no_southland_solar_layer = calgary_no_southland_solar.layers[0]\n",
    "calgary_no_southland_solar_layer_sdf = calgary_no_southland_solar_layer.query().sdf\n",
    "calgary_no_southland_solar_layer_sdf=calgary_no_southland_solar_layer_sdf[['FID','date','ID','solar_plan','altitude_m',\n",
    "                                                                           'latitude','longitude','wind_speed','dayl__s_',\n",
    "                                                                           'prcp__mm_d','srad__W_m_','swe__kg_m_', 'tmax__deg',\n",
    "                                                                           'tmin__deg','vp__Pa_','kWh_filled','capacity_f',\n",
    "                                                                           'SHAPE']]\n",
    "calgary_no_southland_solar_layer_sdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = ['altitude_m', 'wind_speed', 'dayl__s_', 'prcp__mm_d','srad__W_m_','swe__kg_m_','tmax__deg','tmin__deg','vp__Pa_']\n",
    "preprocessors =  [('altitude_m', 'wind_speed', 'dayl__s_', 'prcp__mm_d','srad__W_m_','swe__kg_m_','tmax__deg',\n",
    "                   'tmin__deg','vp__Pa_', RobustScaler())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = prepare_tabulardata(calgary_no_southland_solar_layer,\n",
    "                           'capacity_f',\n",
    "                           explanatory_variables=X,                           \n",
    "                           preprocessors=preprocessors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train model using AutoML\n",
    "<a id='AutoML'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arcgis.learn import AutoML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AutoML class accepts the following paramters:\n",
    "- data (Required Paramter): Returned data object from `prepare_tabulardata` function in the previous step.\n",
    "    \n",
    "- total_time_limit (Optional parameter): It is the total time in seconds that must be used for AutoML training. Default set is 3600 (1 Hr). At the completion of total_time_limit, the training of AutoML completes and the best model trained until then is used. \n",
    "    \n",
    "- mode (Optional Parameter): Model can be either Explain. Perform or Compete. Default is Explain.\n",
    "\n",
    "- algorithms (Optional Parameter): This parameter takes in list of algorithms as input. The algorithms could be subset of the following: Linear,Decision Tree,Random Forest,Extra Trees,LightGBM,Xgboost,Neural Network.\n",
    "\n",
    "- eval_metric (Optional Parameter): The metric to be used to compare models. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AutoML modes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Explain : To to be used when you want to explain and understand the data.\n",
    "                                      Uses 75%/25% train/test split.\n",
    "                                      Uses the following models: Baseline, Linear, Decision Tree,\n",
    "                                      Random Forest, XGBoost, Neural Network, and Ensemble.\n",
    "                                      Has full explanations in reports: learning curves, importance\n",
    "                                      plots, and SHAP plots.\n",
    "- Perform : To be used when you want to train a model that will be used in real-life use cases.\n",
    "                                      Uses 5-fold CV (Cross-Validation).\n",
    "                                      Uses the following models: Linear, Random Forest, LightGBM,\n",
    "                                      XGBoost,Neural Network, and Ensemble.\n",
    "                                      Has learning curves and importance plots in reports.\n",
    "- Compete : To be used for machine learning competitions (maximum performance).\n",
    "                                      Uses 10-fold CV (Cross-Validation).\n",
    "                                      Uses the following models: Decision Tree, Random Forest, Extra Trees,\n",
    "                                      XGBoost, Neural Network, Nearest Neighbors, Ensemble,\n",
    "                                      and Stacking.It has only learning curves in the reports.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "AutoML_class_obj = AutoML(data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After creating the AutoML object by passing the data obtained from `prepare_tabulardata` and using default values for other parameters, now we proceed to train the model using AutoML. This is done by calling the `fit` method as shown below. New folder will be created and all the models and their varients are saved in that folder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Network algorithm was disabled because it doesn't support n_jobs parameter.\n",
      "AutoML directory: ~\\AppData\\Local\\Temp\\scratch\\tmpmbhb97_l\n",
      "The task is regression with evaluation metric rmse\n",
      "AutoML will use algorithms: ['Linear', 'Decision Tree', 'Random Trees', 'Extra Trees', 'LightGBM', 'Xgboost']\n",
      "AutoML will ensemble available models\n",
      "AutoML steps: ['simple_algorithms', 'default_algorithms', 'ensemble']\n",
      "* Step simple_algorithms will try to check up to 2 models\n",
      "1_DecisionTree rmse 0.048893 trained in 3.63 seconds\n",
      "Exception while producing SHAP explanations. 'float' object has no attribute 'shape'\n",
      "Continuing ...\n",
      "2_Linear rmse 0.046288 trained in 1.1 seconds\n",
      "* Step default_algorithms will try to check up to 4 models\n",
      "3_Default_LightGBM rmse 0.028985 trained in 7.23 seconds\n",
      "There was an error during 4_Default_Xgboost training.\n",
      "Please check ~\\AppData\\Local\\Temp\\scratch\\tmpmbhb97_l\\errors.md for details.\n",
      "5_Default_RandomTrees rmse 0.043642 trained in 4.5 seconds\n",
      "6_Default_ExtraTrees rmse 0.044894 trained in 6.55 seconds\n",
      "* Step ensemble will try to check up to 1 model\n",
      "Ensemble rmse 0.028985 trained in 0.28 seconds\n",
      "AutoML fit time: 32.35 seconds\n",
      "AutoML best model: 3_Default_LightGBM\n",
      "All the evaluated models are saved in the path  ~\\AppData\\Local\\Temp\\scratch\\tmpmbhb97_l\n"
     ]
    }
   ],
   "source": [
    "AutoML_class_obj.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the best model is identified after the completion of `fit` method, the model is then saved by calling the `save` method. The transforms and the encoders used on the training data, along with the Esri Model Definition (EMD) file and the dlpk is then saved in the path specified by the user. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "AutoML_class_obj.save('AutoML_class_obj')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the score of the best model, visualize the results on validation dataset and also get predictions on new data using the corresponding methods shown below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.962025310798984"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AutoML_class_obj.score()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1489</th>\n",
       "      <td>1055</td>\n",
       "      <td>0.019555</td>\n",
       "      <td>29376.0</td>\n",
       "      <td>0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>240</td>\n",
       "      <td>5.819128</td>\n",
       "      <td>0.017498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3502</th>\n",
       "      <td>1112</td>\n",
       "      <td>0.253015</td>\n",
       "      <td>53568.0</td>\n",
       "      <td>0</td>\n",
       "      <td>473.600006</td>\n",
       "      <td>0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>680</td>\n",
       "      <td>5.097813</td>\n",
       "      <td>0.019465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4304</th>\n",
       "      <td>1070</td>\n",
       "      <td>0.248061</td>\n",
       "      <td>50112.0</td>\n",
       "      <td>0</td>\n",
       "      <td>422.399994</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>800</td>\n",
       "      <td>3.733651</td>\n",
       "      <td>0.021266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5491</th>\n",
       "      <td>1090</td>\n",
       "      <td>0.018597</td>\n",
       "      <td>34905.601562</td>\n",
       "      <td>0</td>\n",
       "      <td>265.600006</td>\n",
       "      <td>28</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-14.5</td>\n",
       "      <td>200</td>\n",
       "      <td>8.435382</td>\n",
       "      <td>0.016190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7679</th>\n",
       "      <td>1096</td>\n",
       "      <td>0.112015</td>\n",
       "      <td>44582.398438</td>\n",
       "      <td>0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1280</td>\n",
       "      <td>4.886889</td>\n",
       "      <td>0.015759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      altitude_m  capacity_f      dayl__s_  prcp__mm_d  srad__W_m_  \\\n",
       "1489        1055    0.019555       29376.0           0        96.0   \n",
       "3502        1112    0.253015       53568.0           0  473.600006   \n",
       "4304        1070    0.248061       50112.0           0  422.399994   \n",
       "5491        1090    0.018597  34905.601562           0  265.600006   \n",
       "7679        1096    0.112015  44582.398438           0       288.0   \n",
       "\n",
       "      swe__kg_m_  tmax__deg  tmin__deg  vp__Pa_  wind_speed  \\\n",
       "1489           0       -4.5      -12.0      240    5.819128   \n",
       "3502           0       24.5        8.0      680    5.097813   \n",
       "4304           0       29.0        7.5      800    3.733651   \n",
       "5491          28        3.0      -14.5      200    8.435382   \n",
       "7679           0       22.5       10.5     1280    4.886889   \n",
       "\n",
       "      capacity_f_results  \n",
       "1489            0.017498  \n",
       "3502            0.019465  \n",
       "4304            0.021266  \n",
       "5491            0.016190  \n",
       "7679            0.015759  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AutoML_class_obj.show_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>altitude_m</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>dayl__s_</th>\n",
       "      <th>prcp__mm_d</th>\n",
       "      <th>srad__W_m_</th>\n",
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       "      <th>tmax__deg</th>\n",
       "      <th>tmin__deg</th>\n",
       "      <th>vp__Pa_</th>\n",
       "      <th>prediction_results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1095</td>\n",
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       "      <td>12</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>-29.5</td>\n",
       "      <td>40</td>\n",
       "      <td>0.000117</td>\n",
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       "      <td>5.076316</td>\n",
       "      <td>27648.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>12</td>\n",
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       "      <td>-32.0</td>\n",
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       "      <td>27648.0</td>\n",
       "      <td>0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>12</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>-26.5</td>\n",
       "      <td>80</td>\n",
       "      <td>0.000313</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1095</td>\n",
       "      <td>2.561512</td>\n",
       "      <td>27648.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>12</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>-28.5</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1095</td>\n",
       "      <td>3.887439</td>\n",
       "      <td>50457.601562</td>\n",
       "      <td>0</td>\n",
       "      <td>419.200012</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1080</td>\n",
       "      <td>0.218459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1095</td>\n",
       "      <td>5.680981</td>\n",
       "      <td>50457.601562</td>\n",
       "      <td>0</td>\n",
       "      <td>435.200012</td>\n",
       "      <td>0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1240</td>\n",
       "      <td>0.229766</td>\n",
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       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1095</td>\n",
       "      <td>5.332447</td>\n",
       "      <td>50112.0</td>\n",
       "      <td>3</td>\n",
       "      <td>195.199997</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1400</td>\n",
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       "      <th>98</th>\n",
       "      <td>1095</td>\n",
       "      <td>5.281691</td>\n",
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       "      <td>7</td>\n",
       "      <td>288.0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1160</td>\n",
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       "      <td>1</td>\n",
       "      <td>384.0</td>\n",
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       "      <td>24.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    altitude_m  wind_speed      dayl__s_  prcp__mm_d  srad__W_m_  swe__kg_m_  \\\n",
       "0         1095     7.20467       27648.0           1  108.800003          12   \n",
       "1         1095    3.385235       27648.0           1  115.199997          12   \n",
       "2         1095    5.076316       27648.0           0  118.400002          12   \n",
       "3         1095    5.617623       27648.0           0        96.0          12   \n",
       "4         1095    2.561512       27648.0           0  118.400002          12   \n",
       "..         ...         ...           ...         ...         ...         ...   \n",
       "95        1095    3.887439  50457.601562           0  419.200012           0   \n",
       "96        1095    5.680981  50457.601562           0  435.200012           0   \n",
       "97        1095    5.332447       50112.0           3  195.199997           0   \n",
       "98        1095    5.281691  49766.398438           7       288.0           0   \n",
       "99        1095    5.033775  49766.398438           1       384.0           0   \n",
       "\n",
       "    tmax__deg  tmin__deg  vp__Pa_  prediction_results  \n",
       "0       -10.5      -21.0      120           -0.002004  \n",
       "1       -18.0      -29.5       40            0.000117  \n",
       "2       -20.0      -32.0       40            0.001523  \n",
       "3       -18.0      -26.5       80            0.000313  \n",
       "4       -17.0      -28.5       40            0.000577  \n",
       "..        ...        ...      ...                 ...  \n",
       "95       26.0        8.0     1080            0.218459  \n",
       "96       31.5       10.0     1240            0.229766  \n",
       "97       21.0       12.0     1400            0.131819  \n",
       "98       24.5        9.0     1160            0.130322  \n",
       "99       24.0        8.5     1120            0.182289  \n",
       "\n",
       "[100 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AutoML_class_obj.predict(data._dataframe.iloc[:100][X],prediction_type=\"dataframe\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in the case of `MLModel` and `FullyConnectedNetwork`, predictions can also be obtained in the form of feature class and rasters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally it is also possible to generate and view the report of all the models trained by the AutoML, the performance and the hyperparamters used in each of the model variant. The report can be generated using the report method as shown below. The reports, also show the learning curves and feature importance charts for each of the model evaluated during the training.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "AutoML_class_obj.report()"
   ]
  },
  {
   "attachments": {
    "report.PNG": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![report.PNG](attachment:report.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reload trained model for prediction\n",
    "<a id='Reload'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The trained AutoML can be reloaded from the disk to get the predictions on new data. This is done using `from_model` method. This method takes in the path to the emd file as the input. The best model that was identified using the training phase will automatically be picked up and the prediction can be done on the new data using this model by calling the predict method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arcgis.learn import AutoML\n",
    "AutoML_test_reload=AutoML.from_model(r'AutoML_class_obj')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>altitude_m</th>\n",
       "      <th>capacity_f</th>\n",
       "      <th>dayl__s_</th>\n",
       "      <th>prcp__mm_d</th>\n",
       "      <th>srad__W_m_</th>\n",
       "      <th>swe__kg_m_</th>\n",
       "      <th>tmax__deg</th>\n",
       "      <th>tmin__deg</th>\n",
       "      <th>vp__Pa_</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>prediction_results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1095</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>27648.0</td>\n",
       "      <td>1</td>\n",
       "      <td>108.800003</td>\n",
       "      <td>12</td>\n",
       "      <td>-10.5</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>120</td>\n",
       "      <td>7.20467</td>\n",
       "      <td>-0.002004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1095</td>\n",
       "      <td>0.000354</td>\n",
       "      <td>27648.0</td>\n",
       "      <td>1</td>\n",
       "      <td>115.199997</td>\n",
       "      <td>12</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>-29.5</td>\n",
       "      <td>40</td>\n",
       "      <td>3.385235</td>\n",
       "      <td>0.000117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1095</td>\n",
       "      <td>0.00053</td>\n",
       "      <td>27648.0</td>\n",
       "      <td>0</td>\n",
       "      <td>118.400002</td>\n",
       "      <td>12</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>40</td>\n",
       "      <td>5.076316</td>\n",
       "      <td>0.001523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1095</td>\n",
       "      <td>0.000707</td>\n",
       "      <td>27648.0</td>\n",
       "      <td>0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>12</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>-26.5</td>\n",
       "      <td>80</td>\n",
       "      <td>5.617623</td>\n",
       "      <td>0.000313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1095</td>\n",
       "      <td>0.000883</td>\n",
       "      <td>27648.0</td>\n",
       "      <td>0</td>\n",
       "      <td>118.400002</td>\n",
       "      <td>12</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>-28.5</td>\n",
       "      <td>40</td>\n",
       "      <td>2.561512</td>\n",
       "      <td>0.000577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1095</td>\n",
       "      <td>0.242472</td>\n",
       "      <td>50457.601562</td>\n",
       "      <td>0</td>\n",
       "      <td>419.200012</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1080</td>\n",
       "      <td>3.887439</td>\n",
       "      <td>0.218459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1095</td>\n",
       "      <td>0.226795</td>\n",
       "      <td>50457.601562</td>\n",
       "      <td>0</td>\n",
       "      <td>435.200012</td>\n",
       "      <td>0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1240</td>\n",
       "      <td>5.680981</td>\n",
       "      <td>0.229766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1095</td>\n",
       "      <td>0.136033</td>\n",
       "      <td>50112.0</td>\n",
       "      <td>3</td>\n",
       "      <td>195.199997</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>5.332447</td>\n",
       "      <td>0.131819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1095</td>\n",
       "      <td>0.139699</td>\n",
       "      <td>49766.398438</td>\n",
       "      <td>7</td>\n",
       "      <td>288.0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1160</td>\n",
       "      <td>5.281691</td>\n",
       "      <td>0.130322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1095</td>\n",
       "      <td>0.190066</td>\n",
       "      <td>49766.398438</td>\n",
       "      <td>1</td>\n",
       "      <td>384.0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1120</td>\n",
       "      <td>5.033775</td>\n",
       "      <td>0.182289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    altitude_m  capacity_f      dayl__s_  prcp__mm_d  srad__W_m_  swe__kg_m_  \\\n",
       "0         1095    0.000177       27648.0           1  108.800003          12   \n",
       "1         1095    0.000354       27648.0           1  115.199997          12   \n",
       "2         1095     0.00053       27648.0           0  118.400002          12   \n",
       "3         1095    0.000707       27648.0           0        96.0          12   \n",
       "4         1095    0.000883       27648.0           0  118.400002          12   \n",
       "..         ...         ...           ...         ...         ...         ...   \n",
       "95        1095    0.242472  50457.601562           0  419.200012           0   \n",
       "96        1095    0.226795  50457.601562           0  435.200012           0   \n",
       "97        1095    0.136033       50112.0           3  195.199997           0   \n",
       "98        1095    0.139699  49766.398438           7       288.0           0   \n",
       "99        1095    0.190066  49766.398438           1       384.0           0   \n",
       "\n",
       "    tmax__deg  tmin__deg  vp__Pa_  wind_speed  prediction_results  \n",
       "0       -10.5      -21.0      120     7.20467           -0.002004  \n",
       "1       -18.0      -29.5       40    3.385235            0.000117  \n",
       "2       -20.0      -32.0       40    5.076316            0.001523  \n",
       "3       -18.0      -26.5       80    5.617623            0.000313  \n",
       "4       -17.0      -28.5       40    2.561512            0.000577  \n",
       "..        ...        ...      ...         ...                 ...  \n",
       "95       26.0        8.0     1080    3.887439            0.218459  \n",
       "96       31.5       10.0     1240    5.680981            0.229766  \n",
       "97       21.0       12.0     1400    5.332447            0.131819  \n",
       "98       24.5        9.0     1160    5.281691            0.130322  \n",
       "99       24.0        8.5     1120    5.033775            0.182289  \n",
       "\n",
       "[100 rows x 11 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AutoML_test_reload.predict(data._dataframe.iloc[:100],prediction_type=\"dataframe\")"
   ]
  }
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